The platform's core pieces: overall architecture, Delta Lake, and Unity Catalog
The Databricks Data Intelligence Platform combines a unified workspace for data engineering, analytics, and AI on top of open architecture, with Delta Lake as the storage foundation and Unity Catalog as the governance layer. It separates compute from storage and relies on cloud object storage as the single source of truth, avoiding data silos and duplicate copies. Understanding how the control plane, compute plane, Delta Lake, and Unity Catalog fit together is foundational to every other exam domain.
Must-know
- The platform architecture splits into a control plane (Databricks-managed: web app, notebooks, job scheduler, cluster manager) and a compute plane (runs in the customer's cloud account, executes actual data processing) so customer data stays within the customer's cloud environment.
- Delta Lake is an open-source storage layer that brings ACID transactions, schema enforcement, and time travel to data stored as Parquet files in cloud object storage, forming the foundation of the lakehouse.
- Unity Catalog provides a unified governance layer across all workspaces in an account, using a three-level namespace (catalog.schema.table) to organize data and enabling fine-grained access control, auditing, lineage, and data discovery from one place.
- The lakehouse architecture combines the low-cost, flexible storage of a data lake with the ACID transactions and performance features of a data warehouse, eliminating the need to maintain separate systems for BI and ML/AI workloads.
- Compute resources (clusters, SQL warehouses) are ephemeral and can be started/stopped independently of storage, meaning data persists in cloud storage even when no compute is running.
- Unity Catalog's metastore is created at the account level and can be attached to multiple workspaces, so metadata, permissions, and governance policies are consistent across an entire organization rather than siloed per workspace.
A data engineering team at a financial services company must prove to auditors that raw customer records never leave the company's cloud account, even though Databricks fully manages notebooks, job scheduling, and cluster orchestration through a shared web application. Which description of the Databricks architecture confirms that this requirement is already satisfied?
What you have tried across Databricks DEA's objectives, not a readiness score.
Databricks Intelligence Platform
Data Ingestion and Loading
- Batch, streaming, and incremental loading patterns, and where the data comes from
- Loading files from cloud storage into governed tables with COPY INTO
- Landing data with Auto Loader, and handling schema enforcement and evolution
- Setting up Lakeflow Connect to ingest from enterprise sources reliably
- Pulling data through JDBC, ODBC, or REST clients and scheduling the job
- Choosing the right ingestion method for a given volume, frequency, and governance need
- Bringing semi-structured and unstructured data into governed Delta tables
Data Transformation and Modeling
- Cleaning bronze data into silver tables with PySpark and SQL
- Joining and combining DataFrames with the different join and union types
- Reshaping columns, rows, and arrays in a table
- Deduplicating and aggregating DataFrames
- Tuning Spark's core parameters and measuring what changed
- Building Gold-layer views and tables for BI and analytics
- Validating Silver and Gold datasets for quality
Working with Lakeflow Jobs
Implementing CI/CD
- Branching, committing, and opening pull requests from inside the Databricks workspace
- Promoting one codebase across dev, test, and prod with bundle variables and overrides
- Packaging and deploying jobs and pipelines with Automation Bundles
- Validating and managing bundle deployments from the Databricks CLI
Troubleshooting, Monitoring, and Optimization
- Spotting performance trends in a job's run history
- Reading job status, task graphs, and failure rates to monitor pipeline health
- Diagnosing skew, shuffle, and spill from Spark UI stage metrics
- What Liquid Clustering and predictive optimization actually do
- Diagnosing cluster startup failures, library conflicts, and out-of-memory errors
Governance and Security
Coverage checked against the published exam guide on Aug 1, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.